Introduction

Rs measured at mean annual soil temperature

Rs measured at mean annual soil temperature

The objects of this analysis are

3. Methods

Data

Statistics

Update Bahn’s model

3.4.1 Ts sources (MGRsD, MGRsD_TAIR, From paper, Rs_Ts_relationship)

  • We thus exhaustively examed the possibilities cause the Rs_annual_bahn vs Rs_annual not following 1:1 line.

  • At first, we tested the soil temperature sources and its effect on the Rs_annual_bahn vs Rs_annual. The results show that Ts sources do not have clear effects on the Rs_annual_bahn and Rs_annual relationship.

  • From MGRsD means mean annual soil temperature (amst) are from a global monthly soil respiration database, each site has more than 12 months measured soil temperature read from original papers.

  • From paper means amst were reported from the original paper (table, figures, or description).

  • Partly from TAIR means: some studies did not measure soil temperature all year aroud, for those months, we predic soil temperature based on monthly air temperature (Tsoil = 2.918 + 0.829*Tair, this model was developed based on the sites which have >= 12 months soil temperature measurements).

  • Rs_Ts_relationship: there are 67 records I cannot get the soil temperature information through above three methods. Based on the Rs_Ts_relationship and reported Rs_annual, I calculated the amst.

  • The calculated amst was then compared with the annual Tair, if they are well matched (error < 5%), calculated mast were used.

  • Calculated amst and annual Tair not well match usually indicate a potential problem, then I go back to the manuscript and check out what is the problem.

  • Whenever a paper reported annual mean Ts, I compared the reported mast and estimated mast based on the Rs_Ts_relationship, I found they are well matched.

3.4.2 Annual Rs or Ts coverage effect

  • Secondly, we tested whether Ts and Rs coverage (e.g., 0-0.5 means Rs or Ts only measured less than 6 months), the results show that Ts and Rs coverage do not have significant effect on the Rs_annual_bahn vs Rs_annual relationship.

3.4.3 Effect of maximum allowed divergence between global climate data set and site-specific air temperature

  • Thirdly, we tested whether maximum allowed divergence between global climate data set and site-specific air temperature affect the Rs_annual_bahn vs Rs_annual relationship.
  • As we throw out data points with high divergence, R2 and RSE goes up and down, suggesting that the Tair divergence do not have great effects.
  • TAIR_dev = with( srdb, abs( TAnnual_Del - Study_Temp ) )

3.4.4 Effect of maximum allowed divergence between annual precipitation from paper and Del

  • Fourth, we also tested whether maximum allowed divergence between global climate data set and site-specific precipitation affect the Rs_annual_bahn vs Rs_annual relationship.
  • As we throw out data points with high divergence, R2 and RSE show no big changes, suggesting that the precipitation divergence do not have great effects.
  • TAIR_dev = with( srdb, abs( PAnnual_Del - Study_Precip ) )

  • We also compared:
  • MAT from university of Delaware university (MAT_Del) and MAT reported from the papers (a)
  • TAnnual from University Delaware climate data (TAnnual_Del) and annual temperature from papers (study_temp) (b)
  • MAP from university of Delaware university (MAP_Del) and MAP reported from the papers (c)
  • PAnnual from University Delaware climate data (PAnnual_Del) and annual precipitation from papers (study_precip) (d)
  • The temperature and precipitation collected from University of Delaware climate data well matches the data reported from publication.
  • This also support that the divergence between global climate data set and site-specific precipitation/temperature have small effect.

  • We tested the affect of preipitation and temperature divergence, using multiple linear regression, with divergence as chatergorical indicator, and the results also support that precipitation and temperature divergence have no significant effect on the Rs_annual_bahn vs Rs_annual relationship.

3.4.5 Test ecosystem type

  • Rs_annual_ban vs Rs_annual show significant different relationship among different ecosystems.
  • For example, agriculture has lower slope but wetland has higher slope.
  • However, it is unlikely that the data from agriculture and wetland shifting the regression between Rs_annual_bahn vs Rs_annual away from 1:1 line.
  • As when we remove the Ag data, the Rs_annual_bahn vs Rs_annual regression still differ from 1:1 line.

  • And plot Ag alone, we see the Rs_annual_bahn vs Rs_annual regression in Ag does not greatly differ from the rest.
  • We also detected the outlier points (cooks.distance > 0.5), when the outliers removed, the regression show no difference, indicating that outliers do not have large effects.
  • Similar conculsion for the wetland.

3.4.6 Test Rs measure method

  • Different measure method shows no big effects on the Rs_annual vs Rs_annual relationship.

3.4.7 RA- or RH-dominated effect?

  • RA dominated sites tend to have larger intercept than RH dominated sites, but no difference in slope.
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3.4.8 Biome effect?

  • Mediterranean shows big difference as other biomes.

3.4.9 Drought effect?

  • MAP’s affects on the Rs_annual_bahn vs Rs_annual relationship is very limit (p ~~ 0.05).
  • The slope and intercept changes do not followed a clear pattern as MAP changing.
  • MAP is not a good drought index.

  • We then tested standardized drought index (SPI).
  • SPI significantly affect Rs_annual_bahn vs Rs_annual relationship, as SPI decrease (becoming dought), slope decrease, means bahn’s approach tend to overestimate Rs_annual under drought condition.

  • Since SPI comparing annual precipitation in a site with average precipitation over a period (we used 1964-2014), thus SPI characterized drought condition within a year comparing with a long period, but it can not describle the spatial drought condition.
  • We thus also used another drought index: Palmer Drought Severity Index (PDSI) to characterize the spatial drought.
  • The results indicate that PDSI significantly affect the Rs_annual_bahn vs Rs_annual relationship, as it becomes drier, the slope tend to decrease.

4 Update Bhan’s model

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4.2 Using annual air temperature or mean annual temperature (MAT) to calculate Rs at mean temperature (Rs_mat)

  • Since high resolution soil temperature is still lacking, we want to test whether we can use Rs at annual mean air temperature (amat) or mean annual temperature (mat), and then to predict Rs_annual.
  • The results show that the regression between Rs_annual_bahn_amat and Rs_annual away from 1:1 line, with intercept significantly differ from 0 (p<0.001) and slope significantly differ from 1 (p<0.001), note that we tested both new1 and new2 model.
  • Using amat or mat show no big difference, but it is suprize that using mat is even slightly better than using T_Annual.

  • We detected 2 outliers in the Rs_annual_bahn vs Rs_annual regression
  • Remove these two outliers significantly improved the model (slope changed from 0.78 to 0.87, intercept decreased from 222 to 157, however, p values for slope and intercept are still < 0.001).

  • We adjusted the air temperature by TAnnual_adj = 2.918+0.829*TAnnual
  • MAT_adj = 2.918+0.829*MAT
  • The results are better, but still not resolve the problem (p<0.05)

4.3 Re-simulate a model (new3)

  • We re-calculated soil respiration at annual mean air temperature (Rs_amat, i.e., using air temperature rather than soil temperature to calculate soil respiration when air temperature reaches annual mean).
  • Then we re-simulated the relationship between Rs_annual and Rs_amat.
  • Rs_annual = 729.09225 * (Rs_amat ^ 0.46535) + 89.7789 * spi – Medeterrean
  • Rs_annual = 588.618 * ( Rs_amat ^ 0.65022 ) + 22.59026 * spi + 11.29775*pdsi – exclude medeterrean
  • The results show that if we update the model, Rs_annual_bahn_amat can represent Rs_annual.

5. Discussion & questions

Global spatial distribution of soil respiration sites

Global spatial distribution of soil respiration sites

6. More analysis in the future